rules_python
fastapi
rules_python | fastapi | |
---|---|---|
7 | 470 | |
499 | 71,223 | |
1.0% | - | |
9.5 | 9.8 | |
about 19 hours ago | 5 days ago | |
Starlark | Python | |
Apache License 2.0 | MIT License |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
rules_python
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Things I've learned about building CLI tools in Python
What's SV?
I honestly don't know why anyone would use that... as in what does Bazel do better than virtually anything else that can provide this functionality. But, I used to be an ops engineer in a big company which wanted everything to be Maven, regardless of whether it does it well or not. So we built and deployed with Maven a lot of weird and unrelated stuff.
Not impossible, but not anything I'd advise anyone to do on their free time.
Specifically wrt' the link you posted, if you look here: https://github.com/bazelbuild/rules_python/blob/main/python/... it says that only pure Python wheels are supported, but that's also a lie, they don't support half of the functionality of pure Python wheels.
So, definitely not worth using, since lots of functionality is simply not there.
- Python coverage in Bazel has been broken for nearly 6 years
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Build faster with Buck2: Our open source build system
Regarding bazel, the rules_python has a py_wheel rule that helps you creating wheels that you can upload to pypi (https://github.com/bazelbuild/rules_python/blob/52e14b78307a...).
If you want to see an approach of bazel to pypi taken a bit to the extreme you can have a look at tensorflow on GitHub to see how they do it. They don't use the above-mentioned building rule because I think their build step is quite complicated (C/C++ stuff, Vida/ROCm support, python bindings, and multiOS support all in one before you can publish to pypi).
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Incremental Builds for Haskell with Bazel
Python support in Bazel now looks more promising with `rules_python`: https://github.com/bazelbuild/rules_python
`rules_go` to my understanding is great too.
Over years, Bazel is not as opinionated as before, mostly because adoptions in different orgs force it to be so.
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Advantages of Monorepos
I have personally run converted build systems to Bazel, and use it for personal projects as well.
Bazel 1.0 was released in October 2019. If you were using it "a few years ago", I'm guessing you were using a pre-1.0 version. There's not some cutoff where Bazel magically got easy to use, and I still wouldn't describe it as "easy", but the problem it solves is hard to solve well, and the community support for Bazel has gotten a lot better over the past years.
https://github.com/bazelbuild/rules_python
The difficulty and complexity of using Bazel is highly variable. I've seen some projects where using Bazel is just super simple and easy, and some projects where using Bazel required a massive effort (custom toolchains and the like).
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Experimentations on Bazel: Python & FastAPI (1)
load("@bazel_tools//tools/build_defs/repo:http.bzl", "http_archive") #------------------------------------------------------------------------------ # Python #------------------------------------------------------------------------------ # enable python rules http_archive( name = "rules_python", url = "https://github.com/bazelbuild/rules_python/releases/download/0.2.0/rules_python-0.2.0.tar.gz", sha256 = "778197e26c5fbeb07ac2a2c5ae405b30f6cb7ad1f5510ea6fdac03bded96cc6f", )
fastapi
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Github Sponsor Sebastián Ramírez Python programmer
He is probably most well know for creating FastAPI that I taught to some of my clients and Typer that I've never used.
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Python: A SQLAlchemy Wrapper Component That Works With Both Flask and FastAPI Frameworks
It has been an interesting exercise developing this wrapper component. The fact that it seamlessly integrates with the FastAPI framework is just a bonus for me; I didn't plan for it since I hadn't learned FastAPI at the time. I hope you find this post useful. Thank you for reading, and stay safe as always.
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FastAPI Best Practices: A Condensed Guide with Examples
FastAPI is a modern, high-performance web framework for building APIs with Python, based on standard Python type hints.
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Building an Email Assistant Application with Burr
In this tutorial, I will demonstrate how to use Burr, an open source framework (disclosure: I helped create it), using simple OpenAI client calls to GPT4, and FastAPI to create a custom email assistant agent. We’ll describe the challenge one faces and then how you can solve for them. For the application frontend we provide a reference implementation but won’t dive into details for it.
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FastAPI Got Me an OpenAPI Spec Really... Fast
That’s when I found FastAPI.
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How to Deploy a Fast API Application to a Kubernetes Cluster using Podman and Minikube
FastAPI & Uvicorn
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Analysing FastAPI Middleware Performance
Discussion at FastAPI GitHub: https://github.com/tiangolo/fastapi/issues/2696
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LangChain, Python, and Heroku
An API application framework (such as FastAPI)
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Litestar – powerful, flexible, and highly performant Python ASGI framework
It’s been my experience that async Python frameworks tend to turn IO bound problems into CPU bound problems with a high enough request rate, because due to their nature they act as unbounded queues.
This ends up made worse if you’re using sync routes.
If you’re constrained on a resource such as a database connection pool, your framework will continue to pull http requests off the wire that a sane client will cancel and retry due to timeouts because it takes too long to get a connection out of the pool. Since there isn’t a straightforward way to cancel the execution of a route handler in every Python http framework I’ve seen exhibit this problem, the problem quickly snowballs.
This is an issue with fastapi, too- https://github.com/tiangolo/fastapi/issues/5759
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AI-Powered Image Search with CLIP, pgvector, and Fast API
Fast API.
What are some alternatives?
uwsgi-nginx-flask-docker - Docker image with uWSGI and Nginx for Flask applications in Python running in a single container.
AIOHTTP - Asynchronous HTTP client/server framework for asyncio and Python
pip-upgrade - Upgrade your pip packages with one line. A fast, reliable and easy tool for upgrading all of your packages while not breaking any dependencies
HS-Sanic - Async Python 3.6+ web server/framework | Build fast. Run fast. [Moved to: https://github.com/sanic-org/sanic]
black - The uncompromising Python code formatter
Tornado - Tornado is a Python web framework and asynchronous networking library, originally developed at FriendFeed.
python-streams - A Library to support Writing concise functional code in python
django-ninja - 💨 Fast, Async-ready, Openapi, type hints based framework for building APIs
bazel-coverage-report-renderer - Haskell rules for Bazel.
Flask - The Python micro framework for building web applications.
TypeRig - Proxy API and Font Development Toolkit for FontLab
swagger-ui - Swagger UI is a collection of HTML, JavaScript, and CSS assets that dynamically generate beautiful documentation from a Swagger-compliant API.